Abstract
This chapter presents the general background information about the Hierarchical Temporal Memory (HTM). HTM is a recently proposed cognitive learning algorithm that is intended to emulate the overall structural and functionality of the human neocortex responsible for the high-order functions such as cognition, learning and making predictions. The main properties of HTM is hierarchical structure, sparsity and modularity. HTM consists of two main parts: HTM Spatial Pooler (SP) and HTM Temporal Memory (TM). The HTM SP performs the encoding of the input data and produces sparse distributed representation (SDR) of the input pattern useful for visual data processing and classification tasks. The HTM TM detects the temporal changes in the input data and performs prediction making.
Original language | English (US) |
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Title of host publication | Modeling and Optimization in Science and Technologies |
Publisher | Springer [email protected] |
Pages | 169-180 |
Number of pages | 12 |
DOIs | |
State | Published - Jan 1 2020 |
Externally published | Yes |
Bibliographical note
Generated from Scopus record by KAUST IRTS on 2023-09-23ASJC Scopus subject areas
- Modeling and Simulation
- Medical Assisting and Transcription
- Applied Mathematics